MICRODEM computes a series of
parameters for a region, with the region size determined by the user. The
variables include:

ELEV_AVG, ELEV_STD,
ELEV_SKW, ELEV_KRT:
the first four moments of the elevation distribution,
computed with the formulas in Press et al. (1986). ELEV_STD correlates
strongly with slope.

ELEV_MAX:
the maximum elevation in the block. This can find data anomalies and
blunders, in addition to any geomorphic significance.

SLOPE_AVG, SLOPE_STD,
SLOPE_SKW, SLOPE_KRT:
moments of the slope distribution in percent (100*rise/run). Slopes were computed with an eight neighbors unweighted algorithm (Evans,
1998; Florinsky, 1998; Sharpnack and Akin, 1969). The algorithm has little
effect on the results; Guth (1995) and other studies have shown extremely high correlations
between all available slope algorithms. The units (percent or degrees) also
appear to have little impact.

SLOPE_MAX:
largest slope (percent) in the sampling region. While this is largely
of value for detecting blunders during DEM creation, it also has geomorphic
significance.

PLANC_AVG, PLANC _STD,
PLANC _SKW, PLANC _KRT:
moments of the plan curvature distribution, computed with the
formulas in Press et al. (1986). Curvature computed with the equations in
Wood (1996) based on earlier suggestions from Evans.

GAMMA_NS, GAMMA_EW,
GAMMA_NESW, GAMMA_NWSE:
Nugget variance, Co, from the variogram (Curran, 1988). This is
a measure of the elevation difference from each point to its nearest
neighbor in four directions; smaller values reflect smooth terrain, and high
values rougher terrain. All four turn out
to be very highly correlated with each other, and with other measures of
slope.

RoughnessFactor := 1 - sqrt(sqr(x1sq) + sqr(y1sq) +
sqr(z1sq)) / NumPts; (the three numbers are the sums of the squares of
the three components (directional cosines) of the normal vector to the earth's surface)

RELIEF:
difference between the highest and lowest elevations within the sampling
region (Drummond and Dennis, 1968).

Upward and Downward openness moments:

MISSING:
the percentage of holes in the SRTM data. This can be used to filter the
results, and only look at statistics where missing data might bias the
results.